In [1]:
import numpy as np 
import pandas as pd
import scipy.stats as ss
import matplotlib.pyplot as plt
from matplotlib import pylab 
import pickle, glob, os
%matplotlib inline
pylab.rcParams['figure.figsize'] = 16,5

Settings

In [2]:
star_types = ['RRab','RRc','RRd','RRe']
fats_col = ['Amplitude','AndersonDarling','Autocor_length','Beyond1Std','CAR_mean','CAR_sigma','CAR_tau','Con','Eta_e','FluxPercentileRatioMid20','FluxPercentileRatioMid35','FluxPercentileRatioMid50','FluxPercentileRatioMid65','FluxPercentileRatioMid80','Freq1_harmonics_amplitude_0','Freq1_harmonics_amplitude_1','Freq1_harmonics_amplitude_2','Freq1_harmonics_amplitude_3','Freq1_harmonics_rel_phase_0','Freq1_harmonics_rel_phase_1','Freq1_harmonics_rel_phase_2','Freq1_harmonics_rel_phase_3','Freq2_harmonics_amplitude_0','Freq2_harmonics_amplitude_1','Freq2_harmonics_amplitude_2','Freq2_harmonics_amplitude_3','Freq2_harmonics_rel_phase_0','Freq2_harmonics_rel_phase_1','Freq2_harmonics_rel_phase_2','Freq2_harmonics_rel_phase_3','Freq3_harmonics_amplitude_0','Freq3_harmonics_amplitude_1','Freq3_harmonics_amplitude_2','Freq3_harmonics_amplitude_3','Freq3_harmonics_rel_phase_0','Freq3_harmonics_rel_phase_1','Freq3_harmonics_rel_phase_2','Freq3_harmonics_rel_phase_3','LinearTrend','MaxSlope','Mean','Meanvariance','MedianAbsDev','MedianBRP','PairSlopeTrend','PercentAmplitude','PercentDifferenceFluxPercentile','PeriodLS','Period_fit','Psi_CS','Psi_eta','Q31','Rcs','Skew','SlottedA_length','SmallKurtosis','Std','StetsonK','StetsonK_AC']

fats_col_2 = ['Color','Psi_CS','Psi_eta','SlottedA_length','StetsonL','PeriodLs','Rcs','StetsonK_AC','CAR_tau',
          'StetsonK','FluxPercentileRatioMid50','FluxPercentileRatioMid60','CAR_tmean','Skew','Mean','Period_fit',
          'Eta_e','Autocolor_length','FluxPercentileRatioMid35','FluxPercentileRatioMid20','FluxPercentileRatioMid80',
          'Beyond1Std','MedianBRP','SmallKurtosis']

# Extensiones
vista_ext = '.txt'
ogle_ext = '.pkl'
corot_ext = '.csv'

# Points in Corot
corot_n = '3000'
ogle_data = 5000

# Colors to Graph
ogle_color = 'blue'
vista_color = 'red'
corot_colot =  'yellow'

# Outliers
outlier = 0.02

Open Databases

In [3]:
def open_vista(path):
    if os.path.exists(path):
        df = pd.read_csv(path, header=None)
        df.columns = fats_col
        return df
    return pd.DataFrame([])

def read_pkl(path):
    with open(path, 'rb') as f:
        data = pickle.load(f)
    return data 

# Return a Dictionary
def open_ogle(files):
    if len(files) == 0:
        return {}
    data = read_pkl(files[0])
    for key, value in data.items():
        data[key] = [data[key]]

    for f in files[1:]:
        d = read_pkl(f)
        for key, value in d.items():
            data[key] = data[key] + [value]
    return data

def open_corot(files):
    if len(files) == 0:
        return {}
    df = pd.read_csv(files[0])
    
    for f in files[1:]:
        df2 = pd.read_csv(f)      
        df = df.append(df2) 
    
    return df 

Graphs

In [4]:
def plot(title, number, data, min_num, max_num, bin_num = 100):
    plt.subplot(number)
    plt.title(title)
    bins = np.linspace(min_num, max_num, bin_num)
    plt.hist(data, bins, alpha=0.3, normed=True)
    
def plot_together(title, data, min_num, max_num, bin_num = 100):
    bins = np.linspace(min_num, max_num, bin_num)
    plt.figure()
    plt.title(title)
    
    dec = 4
    title_ogle =  'Ogle-III -> Mean: ' + str(round(data[0].mean(),dec)) + ', Std: ' + str(round(data[0].std(),dec))
    title_vista = 'VVVDR4   -> Mean: ' + str(round(data[1].mean(),dec)) + ', Std: ' + str(round(data[1].std(),dec))
    title_corot = 'Corot    -> Mean: ' + str(round(data[2].mean(),dec)) + ', Std: ' + str(round(data[2].std(),dec))

    plt.hist(data[0], normed=True, alpha=0.5, color=ogle_color, label=title_ogle)
    plt.hist(data[1], normed=True, alpha=0.5, color=vista_color, label=title_vista)
    plt.hist(data[2], normed=True, alpha=0.5, color=corot_colot, label=title_corot)
    plt.legend(loc='upper right')
    plt.show()
    
def plot_boxplot(title, data):
    fig = plt.figure(1, figsize=(16, 6))
    ax = fig.add_subplot(111)
    
    plt.title(title)
    
    dec = 4
    title_ogle =  'Ogle-III -> Mean: ' + str(round(data[0].mean(),dec)) + ', Std: ' + str(round(data[0].std(),dec))
    title_vista = 'VVVDR4   -> Mean: ' + str(round(data[1].mean(),dec)) + ', Std: ' + str(round(data[1].std(),dec))
    title_corot = 'Corot    -> Mean: ' + str(round(data[2].mean(),dec)) + ', Std: ' + str(round(data[2].std(),dec))
    
    bp = ax.boxplot(data, patch_artist=True)

    for box in bp['boxes']:
        # change outline color
        box.set( color='#7570b3', linewidth=2)
        # change fill color
        box.set( facecolor = '#1b9e77' )

    ## change color and linewidth of the whiskers
    for whisker in bp['whiskers']:
        whisker.set(color='#7570b3', linewidth=2)

    ## change color and linewidth of the caps
    for cap in bp['caps']:
        cap.set(color='#7570b3', linewidth=2)

    ## change color and linewidth of the medians
    for median in bp['medians']:
        median.set(color='#b2df8a', linewidth=2)

    ## change the style of fliers and their fill
    for flier in bp['fliers']:
        flier.set(marker='o', color='#e7298a', alpha=0.5)
    
    ax.set_xticklabels(['Ogle-III', 'VVVDR4', 'Corot'])
    ax.get_xaxis().tick_bottom()
    ax.get_yaxis().tick_left()
    plt.show()

Load Data

In [5]:
path_vista = ['./VVVDR4/blg/','./VVVDR4/gd/']
path_ogle = ['./OGLE-III/blg/rrlyr/','./OGLE-III/gd/rrlyr/','./OGLE-III/lmc/rrlyr/','./OGLE-III/smc/rrlyr/']
path_corot = './Corot/'

def load_data(idx, star):
    vista_db, ogle_db, corot_db = [],[],[]
    if len(path_vista) > idx:
        vista_db = open_vista(path_vista[idx] + star + vista_ext)
    files = glob.glob(path_ogle[idx] + star + '/*' + ogle_ext)
    ogle_db = open_ogle(files[0:ogle_data])
    files = glob.glob(path_corot + star + '/' + corot_n + '/*' + corot_ext)
    corot_db = open_corot(files)
    
    return vista_db, ogle_db, corot_db

def load_all_data(star, c =ogle_data):
    aux1 = open_vista(path_vista[0] + star + vista_ext)
    aux2 = open_vista(path_vista[1] + star + vista_ext)
    vista_db = aux1.append(aux2)
    
    files = []
    for name in path_ogle:     
        aux = glob.glob(name + star + '/*' + ogle_ext)
        files += aux[0:c]
    ogle_db = open_ogle(files)
    
    files = glob.glob(path_corot + star + '/' + corot_n + '/*' + corot_ext)
    corot_db = open_corot(files)
    
    return vista_db, ogle_db, corot_db

# pcnt = 2% (Remove)
def filter_array(data, pcnt = outlier): 
    data = data[~np.logical_or(np.isnan(data),np.isinf(data))]  
    
    data = pd.Series(data)
    qlow, median, qhigh = data.quantile([pcnt, 0.50, 1-pcnt])
    iqr = qhigh - qlow
    data = data[ (data - median).abs() <= iqr]
    return data.values
In [6]:
def mostrar(vista, ogle, corot):
    for row, name in enumerate(fats_col_2):
        try:          
            hist = plt.figure()
            
            # Databases
            ogle_db = filter_array(np.array(ogle[name]))
            vista_db = filter_array(vista[name].values)
            corot_db = filter_array(corot[name].values)

            print '\t ', row,'.- ', name
            print '\t \t - Ogle-III: N=', len(ogle_db), ' Mean=', ogle_db.mean(), ' Std=', ogle_db.std()
            print '\t \t - VVVDR4:   N=', len(vista_db), ' Mean=', vista_db.mean(), ' Std=', vista_db.std()
            print '\t \t - Corot:    N=', len(corot_db), ' Mean=', corot_db.mean(), ' Std=', corot_db.std()

            plot_boxplot('Boxplot', [ogle_db, vista_db, corot_db])

            min_num = min(min(ogle_db),min(vista_db),min(corot_db))
            max_num = max(max(ogle_db),max(vista_db),max(corot_db))

            plot('Ogle', 131, ogle_db, min_num, max_num)
            plot('Vista', 132, vista_db, min_num, max_num)
            plot('Corot', 133, corot_db, min_num, max_num)
            plt.show()

            plot_together('All Togheter',(ogle_db, vista_db, corot_db), min_num, max_num)
            print '\n'
        except Exception:
            # print '\t \t [!] Fatal Error'
            pass

TODOS

RRc

In [7]:
star = star_types[1]
vista, ogle, corot = load_all_data(star)
mostrar(vista, ogle, corot)
	  1 .-  Psi_CS
	 	 - Ogle-III: N= 10126  Mean= 0.217211227597  Std= 0.0319324423274
	 	 - VVVDR4:   N= 4419  Mean= 0.215087294928  Std= 0.0386197976687
	 	 - Corot:    N= 466  Mean= 0.212766536481  Std= 0.0849473359469
<matplotlib.figure.Figure at 0x1142cbc90>

	  2 .-  Psi_eta
	 	 - Ogle-III: N= 10130  Mean= 0.522242450769  Std= 0.409577184587
	 	 - VVVDR4:   N= 4420  Mean= 1.34728460422  Std= 0.312415823585
	 	 - Corot:    N= 466  Mean= 0.751969566524  Std= 0.54429297202

	  3 .-  SlottedA_length
	 	 - Ogle-III: N= 9968  Mean= 0.945169733146  Std= 0.787923495331
	 	 - VVVDR4:   N= 4404  Mean= 0.130358310586  Std= 0.253276567452
	 	 - Corot:    N= 457  Mean= 0.73097466302  Std= 1.20738739085

	  6 .-  Rcs
	 	 - Ogle-III: N= 10053  Mean= 0.0637583219476  Std= 0.0293822245881
	 	 - VVVDR4:   N= 4414  Mean= 0.163061329216  Std= 0.0449733789698
	 	 - Corot:    N= 466  Mean= 0.2325855  Std= 0.107213389553
<matplotlib.figure.Figure at 0x1160d0210>
<matplotlib.figure.Figure at 0x116915c10>

	  7 .-  StetsonK_AC
	 	 - Ogle-III: N= 10127  Mean= 0.784458773525  Std= 0.0697740584868
	 	 - VVVDR4:   N= 4212  Mean= 0.85070888858  Std= 0.0969742861295
	 	 - Corot:    N= 466  Mean= 0.797023339056  Std= 0.141490694732

	  8 .-  CAR_tau
	 	 - Ogle-III: N= 9942  Mean= 0.348907348848  Std= 0.558646284437
	 	 - VVVDR4:   N= 4416  Mean= 0.5  Std= 0.0
	 	 - Corot:    N= 456  Mean= 8.24875139474  Std= 12.3452345347

	  9 .-  StetsonK
	 	 - Ogle-III: N= 10129  Mean= 0.858866627005  Std= 0.0326929964171
	 	 - VVVDR4:   N= 4390  Mean= 0.810180080057  Std= 0.0423865339217
	 	 - Corot:    N= 461  Mean= 0.786348655098  Std= 0.0518202916915

	  10 .-  FluxPercentileRatioMid50
	 	 - Ogle-III: N= 10131  Mean= 0.582540399588  Std= 0.103201366637
	 	 - VVVDR4:   N= 4420  Mean= 0.421282487297  Std= 0.0942039475626
	 	 - Corot:    N= 466  Mean= 0.446236255365  Std= 0.095965742085

	  13 .-  Skew
	 	 - Ogle-III: N= 10112  Mean= 0.122510539241  Std= 0.133618342256
	 	 - VVVDR4:   N= 4405  Mean= 0.111497774073  Std= 0.643938695804
	 	 - Corot:    N= 462  Mean= -0.134431391775  Std= 0.478064831941
<matplotlib.figure.Figure at 0x1172fe750>
<matplotlib.figure.Figure at 0x1161386d0>

	  14 .-  Mean
	 	 - Ogle-III: N= 10117  Mean= 17.674646671  Std= 1.37742620835
	 	 - VVVDR4:   N= 4400  Mean= 14.5587984113  Std= 0.593024115418
	 	 - Corot:    N= 466  Mean= 13.0255686094  Std= 0.913412554971

	  15 .-  Period_fit
	 	 - Ogle-III: N= 9928  Mean= 2.09061324211e-12  Std= 4.68212581109e-11
	 	 - VVVDR4:   N= 4421  Mean= 0.487832148986  Std= 0.40920032317
	 	 - Corot:    N= 464  Mean= 0.0  Std= 0.0

	  16 .-  Eta_e
	 	 - Ogle-III: N= 9927  Mean= 1156.66691934  Std= 2698.12087132
	 	 - VVVDR4:   N= 4330  Mean= 74409.7060827  Std= 304496.273537
	 	 - Corot:    N= 456  Mean= 0.788192721491  Std= 0.740120354905

	  18 .-  FluxPercentileRatioMid35
	 	 - Ogle-III: N= 10131  Mean= 0.430725938938  Std= 0.095306646715
	 	 - VVVDR4:   N= 4406  Mean= 0.287491716068  Std= 0.0752524508995
	 	 - Corot:    N= 465  Mean= 0.302803058065  Std= 0.0799493716676
<matplotlib.figure.Figure at 0x116858ed0>

	  19 .-  FluxPercentileRatioMid20
	 	 - Ogle-III: N= 10126  Mean= 0.25847676041  Std= 0.0682008868064
	 	 - VVVDR4:   N= 4406  Mean= 0.159699155697  Std= 0.052696621838
	 	 - Corot:    N= 463  Mean= 0.169102786177  Std= 0.0489351185087

	  20 .-  FluxPercentileRatioMid80
	 	 - Ogle-III: N= 10130  Mean= 0.866990769587  Std= 0.054340666722
	 	 - VVVDR4:   N= 4396  Mean= 0.778393467434  Std= 0.0910288293597
	 	 - Corot:    N= 466  Mean= 0.80420583691  Std= 0.0613227328124

	  21 .-  Beyond1Std
	 	 - Ogle-III: N= 10130  Mean= 0.395101005229  Std= 0.062504514429
	 	 - VVVDR4:   N= 4420  Mean= 0.307288263681  Std= 0.0628710557073
	 	 - Corot:    N= 465  Mean= 0.33019475914  Std= 0.0582826263586

	  22 .-  MedianBRP
	 	 - Ogle-III: N= 10129  Mean= 0.262137688418  Std= 0.107802707643
	 	 - VVVDR4:   N= 4409  Mean= 0.373200749996  Std= 0.112970514035
	 	 - Corot:    N= 466  Mean= 0.476842678112  Std= 0.136107012067

	  23 .-  SmallKurtosis
	 	 - Ogle-III: N= 10100  Mean= -0.940960679393  Std= 0.514459399888
	 	 - VVVDR4:   N= 4357  Mean= 0.508062672525  Std= 1.51750933395
	 	 - Corot:    N= 461  Mean= -0.0264601605206  Std= 0.968073900663

RRab

In [8]:
star = star_types[0]
vista, ogle, corot = load_all_data(star)
mostrar(vista, ogle, corot)
	  1 .-  Psi_CS
	 	 - Ogle-III: N= 11928  Mean= 0.216404928492  Std= 0.0244663131439
	 	 - VVVDR4:   N= 10179  Mean= 0.248565605924  Std= 0.040486051988
	 	 - Corot:    N= 27  Mean= 0.215638555556  Std= 0.00896820061366
<matplotlib.figure.Figure at 0x1142cb750>

	  2 .-  Psi_eta
	 	 - Ogle-III: N= 11934  Mean= 0.383704355467  Std= 0.298533805741
	 	 - VVVDR4:   N= 10182  Mean= 0.730582804674  Std= 0.437186867043
	 	 - Corot:    N= 28  Mean= 0.0970224285714  Std= 0.0947183555493

	  3 .-  SlottedA_length
	 	 - Ogle-III: N= 11805  Mean= 1.2014898831  Std= 0.896351816994
	 	 - VVVDR4:   N= 10164  Mean= 0.177785123911  Std= 0.296374275787
	 	 - Corot:    N= 28  Mean= 0.113022464286  Std= 0.0510762078575

	  6 .-  Rcs
	 	 - Ogle-III: N= 11878  Mean= 0.0614192796389  Std= 0.0277888462579
	 	 - VVVDR4:   N= 10170  Mean= 0.159240792533  Std= 0.0451146475059
	 	 - Corot:    N= 27  Mean= 0.0950348518519  Std= 0.0977514216215
<matplotlib.figure.Figure at 0x1165f8cd0>
<matplotlib.figure.Figure at 0x116617850>

	  7 .-  StetsonK_AC
	 	 - Ogle-III: N= 11942  Mean= 0.809674888028  Std= 0.0601009538612
	 	 - VVVDR4:   N= 9704  Mean= 0.857139446749  Std= 0.0910477322619
	 	 - Corot:    N= 28  Mean= 0.866904571429  Std= 0.0308148276813

	  8 .-  CAR_tau
	 	 - Ogle-III: N= 11742  Mean= 0.381042539814  Std= 0.556845511621
	 	 - VVVDR4:   N= 10182  Mean= 0.5  Std= 0.0
	 	 - Corot:    N= 27  Mean= 0.501977888889  Std= 0.204388911

	  9 .-  StetsonK
	 	 - Ogle-III: N= 11952  Mean= 0.832272691575  Std= 0.0210992572892
	 	 - VVVDR4:   N= 10121  Mean= 0.825767094404  Std= 0.0381860541824
	 	 - Corot:    N= 28  Mean= 0.816224964286  Std= 0.0419722427781

	  10 .-  FluxPercentileRatioMid50
	 	 - Ogle-III: N= 11951  Mean= 0.479582328066  Std= 0.0419593290595
	 	 - VVVDR4:   N= 10183  Mean= 0.45967699946  Std= 0.0968797506323
	 	 - Corot:    N= 28  Mean= 0.496302714286  Std= 0.0798127329919

	  13 .-  Skew
	 	 - Ogle-III: N= 11967  Mean= -0.157555011441  Std= 0.289027807369
	 	 - VVVDR4:   N= 10128  Mean= 0.534156426205  Std= 0.469821874534
	 	 - Corot:    N= 28  Mean= -0.476380857143  Std= 0.468316210505
<matplotlib.figure.Figure at 0x116ae8b90>
<matplotlib.figure.Figure at 0x116da3fd0>

	  14 .-  Mean
	 	 - Ogle-III: N= 11935  Mean= 18.0327569663  Std= 1.21846970095
	 	 - VVVDR4:   N= 10156  Mean= 14.3236515639  Std= 0.661586161623
	 	 - Corot:    N= 28  Mean= 12.8856495  Std= 0.901024186309

	  15 .-  Period_fit
	 	 - Ogle-III: N= 11729  Mean= 2.99492264687e-13  Std= 2.44910099949e-12
	 	 - VVVDR4:   N= 10004  Mean= 0.12148704122  Std= 0.279179638377
	 	 - Corot:    N= 28  Mean= 0.0  Std= 0.0

	  16 .-  Eta_e
	 	 - Ogle-III: N= 11730  Mean= 635.611447776  Std= 1554.56510192
	 	 - VVVDR4:   N= 9981  Mean= 36191.6979261  Std= 140057.833619
	 	 - Corot:    N= 28  Mean= 0.0347563571429  Std= 0.0417757589161

	  18 .-  FluxPercentileRatioMid35
	 	 - Ogle-III: N= 11949  Mean= 0.336756784238  Std= 0.0369753433876
	 	 - VVVDR4:   N= 10177  Mean= 0.317854328607  Std= 0.0855886485492
	 	 - Corot:    N= 28  Mean= 0.342228642857  Std= 0.0741824920561
<matplotlib.figure.Figure at 0x1171f6990>

	  19 .-  FluxPercentileRatioMid20
	 	 - Ogle-III: N= 11950  Mean= 0.195531044338  Std= 0.0296411172145
	 	 - VVVDR4:   N= 10164  Mean= 0.176352671117  Std= 0.0616352888195
	 	 - Corot:    N= 28  Mean= 0.195118642857  Std= 0.0520539700566

	  20 .-  FluxPercentileRatioMid80
	 	 - Ogle-III: N= 11967  Mean= 0.832427736121  Std= 0.0379502299181
	 	 - VVVDR4:   N= 10128  Mean= 0.820258907001  Std= 0.0818603241375
	 	 - Corot:    N= 28  Mean= 0.867981642857  Std= 0.0449213251468

	  21 .-  Beyond1Std
	 	 - Ogle-III: N= 11968  Mean= 0.373267275505  Std= 0.037273758292
	 	 - VVVDR4:   N= 10179  Mean= 0.285176360832  Std= 0.0576553896203
	 	 - Corot:    N= 28  Mean= 0.348256607143  Std= 0.0639511181223

	  22 .-  MedianBRP
	 	 - Ogle-III: N= 11964  Mean= 0.321513858754  Std= 0.0775755215565
	 	 - VVVDR4:   N= 10161  Mean= 0.34091400173  Std= 0.111304291385
	 	 - Corot:    N= 27  Mean= 0.282268185185  Std= 0.0877033460669

	  23 .-  SmallKurtosis
	 	 - Ogle-III: N= 11919  Mean= -0.639913535743  Std= 0.341513361734
	 	 - VVVDR4:   N= 10035  Mean= 0.00324150907836  Std= 1.02667641134
	 	 - Corot:    N= 27  Mean= -0.816379851852  Std= 0.358166060082


BLG Y GD

RRab

BLG

In [9]:
star = star_types[0]
vista, ogle, corot = load_data(0,star)
mostrar(vista, ogle, corot)
	  1 .-  Psi_CS
	 	 - Ogle-III: N= 4978  Mean= 0.229251545848  Std= 0.0238417335126
	 	 - VVVDR4:   N= 10171  Mean= 0.248556724441  Std= 0.0404853896984
	 	 - Corot:    N= 27  Mean= 0.215638555556  Std= 0.00896820061366
<matplotlib.figure.Figure at 0x116f91ed0>

	  2 .-  Psi_eta
	 	 - Ogle-III: N= 4930  Mean= 0.198737774409  Std= 0.195690426976
	 	 - VVVDR4:   N= 10174  Mean= 0.730382332476  Std= 0.437170458566
	 	 - Corot:    N= 28  Mean= 0.0970224285714  Std= 0.0947183555493

	  3 .-  SlottedA_length
	 	 - Ogle-III: N= 4978  Mean= 0.812419596224  Std= 0.791772318776
	 	 - VVVDR4:   N= 10156  Mean= 0.177814198447  Std= 0.29639540034
	 	 - Corot:    N= 28  Mean= 0.113022464286  Std= 0.0510762078575

	  6 .-  Rcs
	 	 - Ogle-III: N= 4976  Mean= 0.0697345062531  Std= 0.0361732701985
	 	 - VVVDR4:   N= 10162  Mean= 0.159225929702  Std= 0.0451142988867
	 	 - Corot:    N= 27  Mean= 0.0950348518519  Std= 0.0977514216215
<matplotlib.figure.Figure at 0x1142f6610>
<matplotlib.figure.Figure at 0x11613a410>

	  7 .-  StetsonK_AC
	 	 - Ogle-III: N= 4985  Mean= 0.807519538836  Std= 0.0559897825855
	 	 - VVVDR4:   N= 9696  Mean= 0.857122948054  Std= 0.0910522804058
	 	 - Corot:    N= 28  Mean= 0.866904571429  Std= 0.0308148276813

	  8 .-  CAR_tau
	 	 - Ogle-III: N= 4913  Mean= 0.264427355884  Std= 0.344876454054
	 	 - VVVDR4:   N= 10174  Mean= 0.5  Std= 0.0
	 	 - Corot:    N= 27  Mean= 0.501977888889  Std= 0.204388911

	  9 .-  StetsonK
	 	 - Ogle-III: N= 4985  Mean= 0.843898433261  Std= 0.0187338259856
	 	 - VVVDR4:   N= 10113  Mean= 0.825791002541  Std= 0.0381701815204
	 	 - Corot:    N= 28  Mean= 0.816224964286  Std= 0.0419722427781

	  10 .-  FluxPercentileRatioMid50
	 	 - Ogle-III: N= 4998  Mean= 0.494808090498  Std= 0.0488084474072
	 	 - VVVDR4:   N= 10175  Mean= 0.459697923906  Std= 0.0968680435831
	 	 - Corot:    N= 28  Mean= 0.496302714286  Std= 0.0798127329919

	  13 .-  Skew
	 	 - Ogle-III: N= 4999  Mean= -0.193219768643  Std= 0.315029239199
	 	 - VVVDR4:   N= 10120  Mean= 0.53396376827  Std= 0.46990960547
	 	 - Corot:    N= 28  Mean= -0.476380857143  Std= 0.468316210505
<matplotlib.figure.Figure at 0x1169d5ad0>
<matplotlib.figure.Figure at 0x117046f10>

	  14 .-  Mean
	 	 - Ogle-III: N= 4995  Mean= 16.8145353455  Std= 0.944254513409
	 	 - VVVDR4:   N= 10146  Mean= 14.323412763  Std= 0.659354231723
	 	 - Corot:    N= 28  Mean= 12.8856495  Std= 0.901024186309

	  15 .-  Period_fit
	 	 - Ogle-III: N= 4900  Mean= 1.72038653017e-11  Std= 2.05848419072e-10
	 	 - VVVDR4:   N= 9996  Mean= 0.121410652685  Std= 0.279118201441
	 	 - Corot:    N= 28  Mean= 0.0  Std= 0.0

	  16 .-  Eta_e
	 	 - Ogle-III: N= 4904  Mean= 758.209987217  Std= 1516.57690422
	 	 - VVVDR4:   N= 9974  Mean= 36228.6436475  Std= 140582.607514
	 	 - Corot:    N= 28  Mean= 0.0347563571429  Std= 0.0417757589161

	  18 .-  FluxPercentileRatioMid35
	 	 - Ogle-III: N= 4995  Mean= 0.350891578259  Std= 0.0418493298905
	 	 - VVVDR4:   N= 10169  Mean= 0.317877041187  Std= 0.0855995422881
	 	 - Corot:    N= 28  Mean= 0.342228642857  Std= 0.0741824920561
<matplotlib.figure.Figure at 0x116da7350>

	  19 .-  FluxPercentileRatioMid20
	 	 - Ogle-III: N= 4997  Mean= 0.206245697713  Std= 0.0342884974986
	 	 - VVVDR4:   N= 10156  Mean= 0.17635935888  Std= 0.0616507043625
	 	 - Corot:    N= 28  Mean= 0.195118642857  Std= 0.0520539700566

	  20 .-  FluxPercentileRatioMid80
	 	 - Ogle-III: N= 4997  Mean= 0.850910933117  Std= 0.0398409656075
	 	 - VVVDR4:   N= 10120  Mean= 0.820323580147  Std= 0.0818152817505
	 	 - Corot:    N= 28  Mean= 0.867981642857  Std= 0.0449213251468

	  21 .-  Beyond1Std
	 	 - Ogle-III: N= 4999  Mean= 0.385362973009  Std= 0.0363388630832
	 	 - VVVDR4:   N= 10171  Mean= 0.2851882245  Std= 0.057655762558
	 	 - Corot:    N= 28  Mean= 0.348256607143  Std= 0.0639511181223

	  22 .-  MedianBRP
	 	 - Ogle-III: N= 4992  Mean= 0.274570223315  Std= 0.0679976880316
	 	 - VVVDR4:   N= 10153  Mean= 0.340909359988  Std= 0.111320079532
	 	 - Corot:    N= 27  Mean= 0.282268185185  Std= 0.0877033460669

	  23 .-  SmallKurtosis
	 	 - Ogle-III: N= 4972  Mean= -0.813731607858  Std= 0.289588448216
	 	 - VVVDR4:   N= 10027  Mean= 0.00270892422891  Std= 1.02619849498
	 	 - Corot:    N= 27  Mean= -0.816379851852  Std= 0.358166060082

GD

In [10]:
vista, ogle, corot = load_data(1,star)
mostrar(vista, ogle, corot)
	  1 .-  Psi_CS
	 	 - Ogle-III: N= 35  Mean= 0.218256255126  Std= 0.0210449674164
	 	 - VVVDR4:   N= 8  Mean= 0.259857301089  Std= 0.0397454046001
	 	 - Corot:    N= 27  Mean= 0.215638555556  Std= 0.00896820061366
<matplotlib.figure.Figure at 0x116073110>

	  2 .-  Psi_eta
	 	 - Ogle-III: N= 35  Mean= 0.281994135977  Std= 0.229923958351
	 	 - VVVDR4:   N= 8  Mean= 0.985533323552  Std= 0.379904085862
	 	 - Corot:    N= 28  Mean= 0.0970224285714  Std= 0.0947183555493

	  3 .-  SlottedA_length
	 	 - Ogle-III: N= 36  Mean= 0.0714558333336  Std= 0.0157500684967
	 	 - VVVDR4:   N= 7  Mean= 0.040999999987  Std= 0.029325756588
	 	 - Corot:    N= 28  Mean= 0.113022464286  Std= 0.0510762078575

	  6 .-  Rcs
	 	 - Ogle-III: N= 35  Mean= 0.0616564195676  Std= 0.0323677260203
	 	 - VVVDR4:   N= 8  Mean= 0.178120303569  Std= 0.0414556544854
	 	 - Corot:    N= 27  Mean= 0.0950348518519  Std= 0.0977514216215
<matplotlib.figure.Figure at 0x117364590>
<matplotlib.figure.Figure at 0x116742d50>

	  7 .-  StetsonK_AC
	 	 - Ogle-III: N= 35  Mean= 0.826124015786  Std= 0.0396880352043
	 	 - VVVDR4:   N= 8  Mean= 0.877135865018  Std= 0.082980141151
	 	 - Corot:    N= 28  Mean= 0.866904571429  Std= 0.0308148276813

	  8 .-  CAR_tau
	 	 - Ogle-III: N= 36  Mean= 0.220843540492  Std= 0.115004138836
	 	 - VVVDR4:   N= 8  Mean= 0.5  Std= 0.0
	 	 - Corot:    N= 27  Mean= 0.501977888889  Std= 0.204388911

	  9 .-  StetsonK
	 	 - Ogle-III: N= 36  Mean= 0.835761209833  Std= 0.0178355124427
	 	 - VVVDR4:   N= 8  Mean= 0.795544221356  Std= 0.0455644618781
	 	 - Corot:    N= 28  Mean= 0.816224964286  Std= 0.0419722427781

	  10 .-  FluxPercentileRatioMid50
	 	 - Ogle-III: N= 36  Mean= 0.478688670925  Std= 0.0413034252807
	 	 - VVVDR4:   N= 8  Mean= 0.433063719979  Std= 0.107525571301
	 	 - Corot:    N= 28  Mean= 0.496302714286  Std= 0.0798127329919

	  13 .-  Skew
	 	 - Ogle-III: N= 36  Mean= -0.166899482305  Std= 0.267701208914
	 	 - VVVDR4:   N= 8  Mean= 0.777868714448  Std= 0.23874443358
	 	 - Corot:    N= 28  Mean= -0.476380857143  Std= 0.468316210505
<matplotlib.figure.Figure at 0x116ef51d0>
<matplotlib.figure.Figure at 0x116745550>

	  14 .-  Mean
	 	 - Ogle-III: N= 36  Mean= 17.8596013878  Std= 1.37882362936
	 	 - VVVDR4:   N= 8  Mean= 15.4326511796  Std= 0.67589339866
	 	 - Corot:    N= 28  Mean= 12.8856495  Std= 0.901024186309

	  15 .-  Period_fit
	 	 - Ogle-III: N= 35  Mean= 1.50659634176e-91  Std= 8.78489019344e-91
	 	 - VVVDR4:   N= 7  Mean= 0.107567613261  Std= 0.178738142516
	 	 - Corot:    N= 28  Mean= 0.0  Std= 0.0

	  16 .-  Eta_e
	 	 - Ogle-III: N= 35  Mean= 964.005081578  Std= 1690.85373252
	 	 - VVVDR4:   N= 8  Mean= 166701.64567  Std= 256785.379488
	 	 - Corot:    N= 28  Mean= 0.0347563571429  Std= 0.0417757589161

	  18 .-  FluxPercentileRatioMid35
	 	 - Ogle-III: N= 36  Mean= 0.333406915702  Std= 0.0384209871085
	 	 - VVVDR4:   N= 8  Mean= 0.288983800589  Std= 0.0641931362101
	 	 - Corot:    N= 28  Mean= 0.342228642857  Std= 0.0741824920561
<matplotlib.figure.Figure at 0x116f28e90>

	  19 .-  FluxPercentileRatioMid20
	 	 - Ogle-III: N= 36  Mean= 0.192288342514  Std= 0.0269727585735
	 	 - VVVDR4:   N= 8  Mean= 0.167862555527  Std= 0.0362499540824
	 	 - Corot:    N= 28  Mean= 0.195118642857  Std= 0.0520539700566

	  20 .-  FluxPercentileRatioMid80
	 	 - Ogle-III: N= 36  Mean= 0.83502446111  Std= 0.0341317319415
	 	 - VVVDR4:   N= 8  Mean= 0.738447377155  Std= 0.0965852231169
	 	 - Corot:    N= 28  Mean= 0.867981642857  Std= 0.0449213251468

	  21 .-  Beyond1Std
	 	 - Ogle-III: N= 36  Mean= 0.377633628458  Std= 0.0417549869155
	 	 - VVVDR4:   N= 8  Mean= 0.270093189538  Std= 0.055152415309
	 	 - Corot:    N= 28  Mean= 0.348256607143  Std= 0.0639511181223

	  22 .-  MedianBRP
	 	 - Ogle-III: N= 36  Mean= 0.332032157781  Std= 0.0938008784719
	 	 - VVVDR4:   N= 8  Mean= 0.346804952749  Std= 0.0888434613105
	 	 - Corot:    N= 27  Mean= 0.282268185185  Std= 0.0877033460669

	  23 .-  SmallKurtosis
	 	 - Ogle-III: N= 35  Mean= -0.70288831308  Std= 0.297553763616
	 	 - VVVDR4:   N= 8  Mean= 0.670770044755  Std= 1.35565627337
	 	 - Corot:    N= 27  Mean= -0.816379851852  Std= 0.358166060082


RRc

BLG

In [11]:
star = star_types[1]
vista, ogle, corot = load_data(0,star)
mostrar(vista, ogle, corot)
	  1 .-  Psi_CS
	 	 - Ogle-III: N= 4985  Mean= 0.234641687142  Std= 0.0265801078344
	 	 - VVVDR4:   N= 4417  Mean= 0.215083044236  Std= 0.0386279151753
	 	 - Corot:    N= 466  Mean= 0.212766536481  Std= 0.0849473359469
<matplotlib.figure.Figure at 0x1171d43d0>

	  2 .-  Psi_eta
	 	 - Ogle-III: N= 4925  Mean= 0.248956274961  Std= 0.292686863838
	 	 - VVVDR4:   N= 4418  Mean= 1.3473882548  Std= 0.312445382199
	 	 - Corot:    N= 466  Mean= 0.751969566524  Std= 0.54429297202

	  3 .-  SlottedA_length
	 	 - Ogle-III: N= 4924  Mean= 0.744282325345  Std= 0.691724195731
	 	 - VVVDR4:   N= 4402  Mean= 0.130225124902  Std= 0.253126410103
	 	 - Corot:    N= 457  Mean= 0.73097466302  Std= 1.20738739085

	  6 .-  Rcs
	 	 - Ogle-III: N= 4953  Mean= 0.0688714818416  Std= 0.0361660796259
	 	 - VVVDR4:   N= 4412  Mean= 0.163048207748  Std= 0.0449607998677
	 	 - Corot:    N= 466  Mean= 0.2325855  Std= 0.107213389553
<matplotlib.figure.Figure at 0x116940050>
<matplotlib.figure.Figure at 0x11526b590>

	  7 .-  StetsonK_AC
	 	 - Ogle-III: N= 4988  Mean= 0.804015423483  Std= 0.0646142881681
	 	 - VVVDR4:   N= 4210  Mean= 0.850719768365  Std= 0.0969950485128
	 	 - Corot:    N= 466  Mean= 0.797023339056  Std= 0.141490694732

	  8 .-  CAR_tau
	 	 - Ogle-III: N= 4902  Mean= 0.208040656579  Std= 0.312493004219
	 	 - VVVDR4:   N= 4414  Mean= 0.5  Std= 0.0
	 	 - Corot:    N= 456  Mean= 8.24875139474  Std= 12.3452345347

	  9 .-  StetsonK
	 	 - Ogle-III: N= 4982  Mean= 0.885273235963  Std= 0.0203467268291
	 	 - VVVDR4:   N= 4388  Mean= 0.810165090198  Std= 0.0423638697633
	 	 - Corot:    N= 461  Mean= 0.786348655098  Std= 0.0518202916915

	  10 .-  FluxPercentileRatioMid50
	 	 - Ogle-III: N= 4989  Mean= 0.667246070987  Std= 0.0714133380306
	 	 - VVVDR4:   N= 4418  Mean= 0.421254078827  Std= 0.0941654518403
	 	 - Corot:    N= 466  Mean= 0.446236255365  Std= 0.095965742085

	  13 .-  Skew
	 	 - Ogle-III: N= 4979  Mean= 0.0592716128366  Std= 0.113088697808
	 	 - VVVDR4:   N= 4403  Mean= 0.111435563787  Std= 0.644075955103
	 	 - Corot:    N= 462  Mean= -0.134431391775  Std= 0.478064831941
<matplotlib.figure.Figure at 0x114660dd0>
<matplotlib.figure.Figure at 0x1164b1510>

	  14 .-  Mean
	 	 - Ogle-III: N= 4987  Mean= 16.3996392566  Std= 0.763184668192
	 	 - VVVDR4:   N= 4398  Mean= 14.5582870641  Std= 0.592647775728
	 	 - Corot:    N= 466  Mean= 13.0255686094  Std= 0.913412554971

	  15 .-  Period_fit
	 	 - Ogle-III: N= 4889  Mean= 1.39835643303e-08  Std= 2.37850033644e-07
	 	 - VVVDR4:   N= 4419  Mean= 0.487671854995  Std= 0.409223442738
	 	 - Corot:    N= 464  Mean= 0.0  Std= 0.0

	  16 .-  Eta_e
	 	 - Ogle-III: N= 4891  Mean= 1052.55045517  Std= 2151.9433986
	 	 - VVVDR4:   N= 4329  Mean= 74425.759252  Std= 304529.608763
	 	 - Corot:    N= 456  Mean= 0.788192721491  Std= 0.740120354905

	  18 .-  FluxPercentileRatioMid35
	 	 - Ogle-III: N= 4989  Mean= 0.506111030314  Std= 0.0721233180182
	 	 - VVVDR4:   N= 4404  Mean= 0.287454678873  Std= 0.0751976955078
	 	 - Corot:    N= 465  Mean= 0.302803058065  Std= 0.0799493716676
<matplotlib.figure.Figure at 0x1142d9390>

	  19 .-  FluxPercentileRatioMid20
	 	 - Ogle-III: N= 4989  Mean= 0.309620269064  Std= 0.0571065301447
	 	 - VVVDR4:   N= 4404  Mean= 0.159678420916  Std= 0.0526674940137
	 	 - Corot:    N= 463  Mean= 0.169102786177  Std= 0.0489351185087

	  20 .-  FluxPercentileRatioMid80
	 	 - Ogle-III: N= 4978  Mean= 0.91122968397  Std= 0.0333250805037
	 	 - VVVDR4:   N= 4394  Mean= 0.778425725887  Std= 0.0910020276471
	 	 - Corot:    N= 466  Mean= 0.80420583691  Std= 0.0613227328124

	  21 .-  Beyond1Std
	 	 - Ogle-III: N= 4986  Mean= 0.447235154917  Std= 0.0425037482662
	 	 - VVVDR4:   N= 4418  Mean= 0.307288755629  Std= 0.0628826308387
	 	 - Corot:    N= 465  Mean= 0.33019475914  Std= 0.0582826263586

	  22 .-  MedianBRP
	 	 - Ogle-III: N= 4958  Mean= 0.175723288945  Std= 0.0561389253464
	 	 - VVVDR4:   N= 4407  Mean= 0.373250775484  Std= 0.11294781062
	 	 - Corot:    N= 466  Mean= 0.476842678112  Std= 0.136107012067

	  23 .-  SmallKurtosis
	 	 - Ogle-III: N= 4933  Mean= -1.36110941969  Std= 0.204534491983
	 	 - VVVDR4:   N= 4355  Mean= 0.508523217197  Std= 1.51760105915
	 	 - Corot:    N= 461  Mean= -0.0264601605206  Std= 0.968073900663

GD

In [12]:
vista, ogle, corot = load_data(1,star)
mostrar(vista, ogle, corot)
	  1 .-  Psi_CS
	 	 - Ogle-III: N= 9  Mean= 0.23969910175  Std= 0.00628525098946
	 	 - VVVDR4:   N= 2  Mean= 0.224474948287  Std= 0.00429667547856
	 	 - Corot:    N= 466  Mean= 0.212766536481  Std= 0.0849473359469
<matplotlib.figure.Figure at 0x116e38d50>

	  2 .-  Psi_eta
	 	 - Ogle-III: N= 9  Mean= 0.0894556492299  Std= 0.0592365628661
	 	 - VVVDR4:   N= 2  Mean= 1.11832047554  Std= 0.0659927533537
	 	 - Corot:    N= 466  Mean= 0.751969566524  Std= 0.54429297202

	  3 .-  SlottedA_length
	 	 - Ogle-III: N= 9  Mean= 0.0541577777767  Std= 0.00918031441893
	 	 - VVVDR4:   N= 2  Mean= 0.423499999866  Std= 0.381499999879
	 	 - Corot:    N= 457  Mean= 0.73097466302  Std= 1.20738739085

	  6 .-  Rcs
	 	 - Ogle-III: N= 8  Mean= 0.0350411101019  Std= 0.00601983914122
	 	 - VVVDR4:   N= 2  Mean= 0.192007286241  Std= 0.0606628799206
	 	 - Corot:    N= 466  Mean= 0.2325855  Std= 0.107213389553
<matplotlib.figure.Figure at 0x1148d4350>
<matplotlib.figure.Figure at 0x115958910>

	  7 .-  StetsonK_AC
	 	 - Ogle-III: N= 9  Mean= 0.878985273538  Std= 0.0225518939895
	 	 - VVVDR4:   N= 2  Mean= 0.827806939824  Std= 0.0200466830306
	 	 - Corot:    N= 466  Mean= 0.797023339056  Std= 0.141490694732

	  8 .-  CAR_tau
	 	 - Ogle-III: N= 9  Mean= 0.199379492845  Std= 0.0701463449987
	 	 - VVVDR4:   N= 2  Mean= 0.5  Std= 0.0
	 	 - Corot:    N= 456  Mean= 8.24875139474  Std= 12.3452345347

	  9 .-  StetsonK
	 	 - Ogle-III: N= 9  Mean= 0.893708295297  Std= 0.0131830340617
	 	 - VVVDR4:   N= 2  Mean= 0.843067830924  Std= 0.0702050482754
	 	 - Corot:    N= 461  Mean= 0.786348655098  Std= 0.0518202916915

	  10 .-  FluxPercentileRatioMid50
	 	 - Ogle-III: N= 9  Mean= 0.693007582943  Std= 0.0489405234075
	 	 - VVVDR4:   N= 2  Mean= 0.484036796537  Std= 0.144751082251
	 	 - Corot:    N= 466  Mean= 0.446236255365  Std= 0.095965742085

	  13 .-  Skew
	 	 - Ogle-III: N= 9  Mean= 0.0273939633885  Std= 0.0296958568365
	 	 - VVVDR4:   N= 2  Mean= 0.248453718218  Std= 0.0817559115276
	 	 - Corot:    N= 462  Mean= -0.134431391775  Std= 0.478064831941
<matplotlib.figure.Figure at 0x1171f7950>
<matplotlib.figure.Figure at 0x11534a510>

	  14 .-  Mean
	 	 - Ogle-III: N= 9  Mean= 16.398813862  Std= 1.21252770872
	 	 - VVVDR4:   N= 2  Mean= 15.6832510435  Std= 0.260646392367
	 	 - Corot:    N= 466  Mean= 13.0255686094  Std= 0.913412554971

	  15 .-  Period_fit
	 	 - Ogle-III: N= 8  Mean= 2.23952804266e-311  Std= 0.0
	 	 - VVVDR4:   N= 2  Mean= 0.842001721936  Std= 0.0120317380418
	 	 - Corot:    N= 464  Mean= 0.0  Std= 0.0
/Users/Carlos/Desktop/ExpI/env/lib/python2.7/site-packages/numpy/lib/function_base.py:730: RuntimeWarning: overflow encountered in double_scalars
  norm = bins / (mx - mn)
/Users/Carlos/Desktop/ExpI/env/lib/python2.7/site-packages/numpy/lib/function_base.py:755: RuntimeWarning: invalid value encountered in multiply
  tmp_a *= norm
	  16 .-  Eta_e
	 	 - Ogle-III: N= 9  Mean= 792.438116817  Std= 922.781791365
	 	 - VVVDR4:   N= 1  Mean= 4915.53615753  Std= 0.0
	 	 - Corot:    N= 456  Mean= 0.788192721491  Std= 0.740120354905
<matplotlib.figure.Figure at 0x11671bc10>

	  18 .-  FluxPercentileRatioMid35
	 	 - Ogle-III: N= 9  Mean= 0.532562038132  Std= 0.0526993241675
	 	 - VVVDR4:   N= 2  Mean= 0.369047619048  Std= 0.130952380952
	 	 - Corot:    N= 465  Mean= 0.302803058065  Std= 0.0799493716676
<matplotlib.figure.Figure at 0x118321890>

	  19 .-  FluxPercentileRatioMid20
	 	 - Ogle-III: N= 9  Mean= 0.329234360063  Std= 0.0349590163733
	 	 - VVVDR4:   N= 2  Mean= 0.205357142857  Std= 0.0863095238095
	 	 - Corot:    N= 463  Mean= 0.169102786177  Std= 0.0489351185087

	  20 .-  FluxPercentileRatioMid80
	 	 - Ogle-III: N= 9  Mean= 0.923549122386  Std= 0.018238879161
	 	 - VVVDR4:   N= 2  Mean= 0.707521645022  Std= 0.118235930736
	 	 - Corot:    N= 466  Mean= 0.80420583691  Std= 0.0613227328124

	  21 .-  Beyond1Std
	 	 - Ogle-III: N= 9  Mean= 0.465140793156  Std= 0.0362284787233
	 	 - VVVDR4:   N= 2  Mean= 0.306201550388  Std= 0.0271317829457
	 	 - Corot:    N= 465  Mean= 0.33019475914  Std= 0.0582826263586

	  22 .-  MedianBRP
	 	 - Ogle-III: N= 9  Mean= 0.159505547124  Std= 0.0313162489029
	 	 - VVVDR4:   N= 2  Mean= 0.262969588551  Std= 0.109123434705
	 	 - Corot:    N= 466  Mean= 0.476842678112  Std= 0.136107012067

	  23 .-  SmallKurtosis
	 	 - Ogle-III: N= 9  Mean= -1.44480615666  Std= 0.0958506651042
	 	 - VVVDR4:   N= 2  Mean= -0.494773351121  Std= 0.830954359938
	 	 - Corot:    N= 461  Mean= -0.0264601605206  Std= 0.968073900663

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